Learning to Plan Semantic Free-Space Boundary

Recently, free-space detection has attracted widespread attention. Most existing methods treat free-space detection as a semantic segmentation task. In this paper, we propose a novel approach to directly infer the boundary of the semantic free-space from a single image. Firstly, we design a multistage CNN to produce 2D belief maps with high resolution for boundary segments of different semantic classes, such as road boundary, vertical obstacles on road and so on. The proposed CNN architecture can implicitly learn boundary structure and long-range spatial context. Then, based on the 2D belief maps we address the semantic free-space detection as a dynamic programming problem to ensure the spatial smoothness of the predicted boundary. The experimental results on our dataset show that our method has a convincing performance on various quantitative metrics.